Extension of an iterative closest point algorithm for simultaneous localization and mapping in corridor environments

Abstract

Three-dimensional (3-D) simultaneous localization and mapping (SLAM) is a crucial technique for intelligent robots to navigate autonomously and execute complex tasks. It can also be applied to shape measurement, reverse engineering, and many other scientific or engineering fields. A widespread SLAM algorithm, named KinectFusion, performs well in environments with complex shapes. However, it cannot handle translation uncertainties well in highly structured scenes. This paper improves the KinectFusion algorithm and makes it competent in both structured and unstructured environments. 3-D line features are first extracted according to both color and depth data captured by Kinect sensor. Then the lines in the current data frame are matched with the lines extracted from the entire constructed world model. Finally, we fuse the distance errors of these linepairs into the standard KinectFusion framework and estimate sensor poses using an iterative closest pointbased algorithm. Comparative experiments with the KinectFusion algorithm and one state-of-the-art method in a corridor scene have been done. The experimental results demonstrate that after our improvement, the KinectFusion algorithm can also be applied to structured environments and has higher accuracy.Experiments on two open access datasets further validated our improvements.

Handheld 3D reconstruction based on closed-loop detection and nonlinear optimization

“…However, ORB-SLAM still has some shortcomings. The algorithm is not robust enough for scenes where the feature points are sparse, and the reconstructed map is sparse [14][15][16][17][18].…”

A High Spatial Resolution Depth Sensing Method Based on Binocular Structured Light

“…At present, human computer interaction based on three-dimensional (3D) depth information has become highly attractive in the areas of image processing and computer vision, which further promotes the development of 3D depth acquisition technology. In addition, the recently developed 3D depth sensors, such as the Microsoft Kinect (Microsoft Corporation, Redmond, Washington, DC, USA) [ 1 ], have been applied in more fields, such as gesture recognition [ 2 , 3 , 4 , 5 ], intelligent driving [ 6 , 7 ], surveillance [ 8 ], 3D reconstruction [ 9 , 10 ], and so on. 3D depth acquisition technology measures the distance information between objects and a depth sensor.…”